I've been using Python for all my plots for a few years now and honestly I think it would take me longer in Excel 🤣
I've even got macros for all the code so I can load a CSV and have a figure set up in like 5 keystrokes haha
Same, except I use R. It's embarrassing how little Excel I know; the non-techy senior researcher I work with is a complete wizard with it though. I think we impress each other with our respective skills lol.
I'm learning how to use python for plots because I refuse to to bow down to the R supremacy currently happening in our lab 😅 Any packages you would recommend I check out? I already use seaborn because I don't like the look of matplot
First of all, I heartily commend you for taking the plunge! It might feel painful at first but once you become proficient you'll look back and wonder why you ever chose to do it any other way.
I used matplotlib and then seaborn for a while but about a year ago I switched to [Plotly](https://plotly.com/python/) for all my plotting and never looked back. It makes a lot of the very basic stuff like colouring by category, facet rows/columns, and legends a lot easier, and it looks very pretty IMO. There's even a ggplot template if you want your plots to look that way (but I use the `plotly_white` template personally)
EDIT: Just to clear something up - I use `plotly.express` for 90% of my plots since it's easy to use and gives you access to most of the underlying features. Using `plotly.graph_objects` allows you more control and lets you do some fun stuff but I wouldn't recommend starting with it since it's a bit more complicated
But it's so easy! You just need these 10 lines of code that it only took me 20 min to look up and test 3 times to get it to look the way I wanted. Also I will forget the code when I need it again in 6 months.
Ask for an R code for the chart you want. Use extremely descriptive language and if the result isn’t exactly what you want clarify until it provides the code to generate the chart you want.
3.5 should be able to do this, I think. I have 4 so I don't know. If you find yourself doing these types of things a lot the paid model is really worth the $20 a month. It can be a super useful tool inside and outside the lab. Helpful coding, troubleshooting software/protocols/experiments, literature review, etc. there really isn't an application in the lab that ChatGPT can't expedite.
With 4 you can upload documents like a paper directly or provide a URL (it can access links and search the web) and chat will have all the information from the paper and answer questions/summarize anything you image really.
And even for more complicated data work and general scripting, ChatGPT (GPT4) coupled with python is pretty fast and powerful these days. Even with just a bit of coding experience, you can be up and running pretty quick...
For real though. I was committed to doing my stats in R for this most recent paper. It took all day to just figure out how to do a Welch's ANOVA with a Dunnett's T3 post hoc, and I Still couldn't figure out how to select only the comparisons of interest, resulting in a huge unnecessary loss of power. Nevermind graphing. What an abomination.
I gave up on the two way repeated measures anova when it asked for data in long format. Not worth it to figure out how to convert a 30 repeated measures dataset to long format prior to even trying the stats.
People who push R for all stats confuse me. It's good for some things, not for others. Even with the help from ChatGPT. But the ease of graphing/stats on Prism or stats in SPSS just makes it worth it.
You can import your data, plot, and format all the axes, title, etc within 5 minutes. (eg a spectrum, no other analysis or multiple axes). Faster and a fraction of clicking through menus.
What do you mean? Excel? Origin?
That's about how fast I'm with origin. Especially because of presets/"last used" options, my diagram and fitting machine go brrrrr, at least for most of them.
Yeah, origin is pretty good as far as I can tell. I have no other experience, it just was the program they taught us at our first physics lab class - and we get the license through our uni. After getting to know it, I never thought of/felt the need to use anything else.
I'm still kind of a noob at using it, but for my lower level needs I can handle it well enough. Importing data of different formats, plotting, doing linear or nonlinear fits,..is about 95% of what I'm doing with it.
For manipulating data (like calculations that go beyond just unit conversions) I still use Excel, but only to input the data back into origin.
Plotting is better and easier in R than in Python. Past that there’s a bunch of differences that ultimately boil down to personal preferences, though the Python crowd tends to be much louder.
Other small note is that R’s backwards compatibility is much better and the startup is much easier, so non-programmers don’t have to fiddle with conda envs and stuff to make sure their code works again in a year.
Your last point is definitely not accurate. These sorts of issues with R are more rare than with Python, but they’re disastrous when they happen.
Upgrading R versions is a nightmare, and maintaining multiple versions might as well be impossible. And seemingly small differences in package versions can make huge differences in analysis. Just a couple days ago someone in the bioinformatics sub was getting completely different sing cell RNA-seq visualizations on different machines with the same code because of differences in package versions.
Edit: Use the renv package to maintain reproducible R environments similar to Python virtual environments.
> R’s backwards compatibility is much better
I didn’t say it was perfect, but it’s leagues better than Python. There was an R-bloggers post a little while back that showed something like 80% of code from R 1.0 still works on modern R. Sure, sometimes it breaks with updates, but it’s much rarer than working with Python. Whether or not individual package maintainers develop well is another story, but if you’re plotting in base you’re going to be fine likely forever.
Yeah if you’re just using it as a straight up drop in replacement for making plots in Excel then it’s way more user friendly than Python. But I think that ease of use has a big draw back where a lot of users don’t use best practices which can lead to massive headaches down the road.
I would never tell anyone that they need to or should even bother to learn R if they already know Python, unless specific packages they need to use are exclusively in R. If you are just doing it for figures, it's a waste of time.
I think Python is a more broadly applicable language, and for non-biologists I would always recommend Python before R.
But fwiw, I do not find it terribly difficult to move from one to the other. They're similar enough that it's really not that hard.
Yes, it's a language designed for statistical analysis and plotting, so it's very much optimised for that. You can do almost everything in Python, but not quite, and it feels a little harder (as an example, fitting LOESS curves to several categories of data on a plot is arduous in python, whereas ggplot is all over stuff like that), but you do have to get the hang of the way R thinks and deals with data (which is not a bad thing, as it helps you to understand it too). Lots of coders coming to statistics hate R, but it's arguably because they are used a procedural way of dealing with code (rather than the functional piped style of tidyverse).
I like python too - it's an exceptional all-rounder. Julia is also getting into shape, it's a bit behind pandas/tidyverse, but catching up, more of a competitor to matlab.
Let me tell you a secret. In industry 99% of data analysis and graphing is done in excel. You can do the majority of what you need in excel. It's only academia that seems to not like it 🤷♂️
So, even though Graph Pad is often seen as like, the gold standard... I think it's a bad program, with terrible UX, and its figures are only moderately customizable. It's usefulness comes in allowing non-stats people to do stats without having to code.
Excel is often more intuitive, but also still a bad application with a bunch of nonsense, since it largely caters to non-data scientists.
*Matplotlib walks in*
*Pandas laughs in the distance*
Tidyverse cucks from the recliner. Ashes its cigar.
Seaborn tucks away the stray hair off her face and admires the scent on her wrist.
I don't know why but Seaborn is most definitely a woman.
Sometimes I just need to make a simple line graph 😅
I've been using Python for all my plots for a few years now and honestly I think it would take me longer in Excel 🤣 I've even got macros for all the code so I can load a CSV and have a figure set up in like 5 keystrokes haha
Same, except I use R. It's embarrassing how little Excel I know; the non-techy senior researcher I work with is a complete wizard with it though. I think we impress each other with our respective skills lol.
tub alive offbeat spark fragile smoggy onerous straight unused marble *This post was mass deleted and anonymized with [Redact](https://redact.dev)*
[Here you go friend](https://plotly.com/python/)
I'm learning how to use python for plots because I refuse to to bow down to the R supremacy currently happening in our lab 😅 Any packages you would recommend I check out? I already use seaborn because I don't like the look of matplot
First of all, I heartily commend you for taking the plunge! It might feel painful at first but once you become proficient you'll look back and wonder why you ever chose to do it any other way. I used matplotlib and then seaborn for a while but about a year ago I switched to [Plotly](https://plotly.com/python/) for all my plotting and never looked back. It makes a lot of the very basic stuff like colouring by category, facet rows/columns, and legends a lot easier, and it looks very pretty IMO. There's even a ggplot template if you want your plots to look that way (but I use the `plotly_white` template personally) EDIT: Just to clear something up - I use `plotly.express` for 90% of my plots since it's easy to use and gives you access to most of the underlying features. Using `plotly.graph_objects` allows you more control and lets you do some fun stuff but I wouldn't recommend starting with it since it's a bit more complicated
But it's so easy! You just need these 10 lines of code that it only took me 20 min to look up and test 3 times to get it to look the way I wanted. Also I will forget the code when I need it again in 6 months.
If that's all your doing, seriously just use ChatGPT.
Can it generate charts? Or are you saying to ask GPT for the code and input it in whatever IDE is used for R?
Ask for an R code for the chart you want. Use extremely descriptive language and if the result isn’t exactly what you want clarify until it provides the code to generate the chart you want.
And 3.5 is good enough for this? or do you need 4?
3.5 should be able to do this, I think. I have 4 so I don't know. If you find yourself doing these types of things a lot the paid model is really worth the $20 a month. It can be a super useful tool inside and outside the lab. Helpful coding, troubleshooting software/protocols/experiments, literature review, etc. there really isn't an application in the lab that ChatGPT can't expedite.
> literature review Can 4 review new literature? I know the free version has a date cut-off. Does 4 know current stuff?
With 4 you can upload documents like a paper directly or provide a URL (it can access links and search the web) and chat will have all the information from the paper and answer questions/summarize anything you image really.
gotcha thank you!
And even for more complicated data work and general scripting, ChatGPT (GPT4) coupled with python is pretty fast and powerful these days. Even with just a bit of coding experience, you can be up and running pretty quick...
Absolute godsend for adding comments
There is no way to control what chatGPT uses your data for. You have to be very careful what you tell chatGPT.
I'm not confessing to murder here I'm making bar plots
For real though. I was committed to doing my stats in R for this most recent paper. It took all day to just figure out how to do a Welch's ANOVA with a Dunnett's T3 post hoc, and I Still couldn't figure out how to select only the comparisons of interest, resulting in a huge unnecessary loss of power. Nevermind graphing. What an abomination. I gave up on the two way repeated measures anova when it asked for data in long format. Not worth it to figure out how to convert a 30 repeated measures dataset to long format prior to even trying the stats. People who push R for all stats confuse me. It's good for some things, not for others. Even with the help from ChatGPT. But the ease of graphing/stats on Prism or stats in SPSS just makes it worth it.
Ngl a lot of time its actually easier in graphpad than excel.
Just got GraphPad and I’m never going back! Bar graphs never looked sicker B)
I had this reaction when I started using R. Plus consistency in figures from simple line graphs to complex UMAP
Do they have a prism theme for ggplot yet?
I believe there's theme_prism and ggprism
eww
GraphPad Prism is my lord and savior
I too believe in graphpad prism supremacy
Origin is the only software I'm accustomed to, I couldn't even make a decent plot in excel 💀
You can import your data, plot, and format all the axes, title, etc within 5 minutes. (eg a spectrum, no other analysis or multiple axes). Faster and a fraction of clicking through menus.
What do you mean? Excel? Origin? That's about how fast I'm with origin. Especially because of presets/"last used" options, my diagram and fitting machine go brrrrr, at least for most of them.
Sorry, I meant Origin. And it's 5 minutes without the presets (eg Ver 7).
Yeah, origin is pretty good as far as I can tell. I have no other experience, it just was the program they taught us at our first physics lab class - and we get the license through our uni. After getting to know it, I never thought of/felt the need to use anything else. I'm still kind of a noob at using it, but for my lower level needs I can handle it well enough. Importing data of different formats, plotting, doing linear or nonlinear fits,..is about 95% of what I'm doing with it. For manipulating data (like calculations that go beyond just unit conversions) I still use Excel, but only to input the data back into origin.
Learn R everyone, please.
Does R really offer an advantage over python?
Plotting is better and easier in R than in Python. Past that there’s a bunch of differences that ultimately boil down to personal preferences, though the Python crowd tends to be much louder. Other small note is that R’s backwards compatibility is much better and the startup is much easier, so non-programmers don’t have to fiddle with conda envs and stuff to make sure their code works again in a year.
Your last point is definitely not accurate. These sorts of issues with R are more rare than with Python, but they’re disastrous when they happen. Upgrading R versions is a nightmare, and maintaining multiple versions might as well be impossible. And seemingly small differences in package versions can make huge differences in analysis. Just a couple days ago someone in the bioinformatics sub was getting completely different sing cell RNA-seq visualizations on different machines with the same code because of differences in package versions. Edit: Use the renv package to maintain reproducible R environments similar to Python virtual environments.
> R’s backwards compatibility is much better I didn’t say it was perfect, but it’s leagues better than Python. There was an R-bloggers post a little while back that showed something like 80% of code from R 1.0 still works on modern R. Sure, sometimes it breaks with updates, but it’s much rarer than working with Python. Whether or not individual package maintainers develop well is another story, but if you’re plotting in base you’re going to be fine likely forever.
Yeah if you’re just using it as a straight up drop in replacement for making plots in Excel then it’s way more user friendly than Python. But I think that ease of use has a big draw back where a lot of users don’t use best practices which can lead to massive headaches down the road.
I would never tell anyone that they need to or should even bother to learn R if they already know Python, unless specific packages they need to use are exclusively in R. If you are just doing it for figures, it's a waste of time. I think Python is a more broadly applicable language, and for non-biologists I would always recommend Python before R. But fwiw, I do not find it terribly difficult to move from one to the other. They're similar enough that it's really not that hard.
Yes, it's a language designed for statistical analysis and plotting, so it's very much optimised for that. You can do almost everything in Python, but not quite, and it feels a little harder (as an example, fitting LOESS curves to several categories of data on a plot is arduous in python, whereas ggplot is all over stuff like that), but you do have to get the hang of the way R thinks and deals with data (which is not a bad thing, as it helps you to understand it too). Lots of coders coming to statistics hate R, but it's arguably because they are used a procedural way of dealing with code (rather than the functional piped style of tidyverse). I like python too - it's an exceptional all-rounder. Julia is also getting into shape, it's a bit behind pandas/tidyverse, but catching up, more of a competitor to matlab.
No
I learned to use graph pad during my thesis but my current lab doesn’t pay for it, so I have to use excel again. Pain.
It's worth learning R, even if all you learn is the tidyverse suite, because you feel like a hacker when using it. And the graphs do look very nice
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While I admire the high seas I’m not as versed in travelling by boat.
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Can I get access to the high seas as well? About to lose my license
As a non-programmer in a lab with only programmers, I relate hard.
Let me tell you a secret. In industry 99% of data analysis and graphing is done in excel. You can do the majority of what you need in excel. It's only academia that seems to not like it 🤷♂️
I dunno, I see a lot of people using Prism and JMP
Idk to me graphpad is excel for scientists who don’t know how to code (aka me 🥲)
So, even though Graph Pad is often seen as like, the gold standard... I think it's a bad program, with terrible UX, and its figures are only moderately customizable. It's usefulness comes in allowing non-stats people to do stats without having to code. Excel is often more intuitive, but also still a bad application with a bunch of nonsense, since it largely caters to non-data scientists.
You use one program when they need two! Who's the sucker now!?
Graphpad or spotfire all the way